class_docstr: A neural-network based bandit agent for multi-objective optimization.
  
  This agent receives multiple neural networks. Each network will be trained by
  the agent to predict a specific objective. The agent also receives a
  Scalarizer, which transforms multiple predicted objectives to a single reward.
  The action is chosen greedily by the policy with respect to the scalarized
  predicted reward.
code: def _variables_to_train(self):
      variables_to_train = tf.nest.flatten(
          [net.trainable_variables for net in self._objective_networks]
      )
      return variables_to_train
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Callable, Optional, Sequence, Text, Tuple
  from absl import logging
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.multi_objective import multi_objective_scalarizer
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import greedy_multi_objective_neural_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks.network import Network
  from tf_agents.policies import utils
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
label_desc: Use image node_id 3 for calling the GreedyMultiObjectiveNeuralAgent obj's underlying member method code with example usage: obj._variables_to_train() and returns: variables_to_train